Preamble

# Clear workspace
rm(list=ls()); graphics.off()

### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.
library(magrittr) # For extra-piping operators (eg. %<>%)
library(skimr) # For nice data summaries

The InsideAirBnB data

Instroduction

The data is sourced from the Inside Airbnb which hosts publicly available data from the Airbnb site.
Interactive visualizations are provided here

The dataset comprises of three main tables:

listings - Detailed listings data showing 96 atttributes for each of the listings. Some of the attributes which are intuitivly interesting are: price (continuous), longitude (continuous), latitude (continuous), listing_type (categorical), is_superhost (categorical), neighbourhood (categorical), ratings (continuous) among others.
reviews - Detailed reviews given by the guests with 6 attributes. Key attributes include date (datetime), listing_id (discrete), reviewer_id (discrete) and comment (textual).
calendar - Provides details about booking for the next year by listing. Four attributes in total including listing_id (discrete), date (datetime), available (categorical) and price (continuous).

Load data

listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/listings.csv.gz')
listings %>% glimpse()

Rows: 28,523
Columns: 106
$ id                                           <dbl> 6983, 26057, 26473, 29118, 29618, 31094, 32379, 32841, 33850, 3…
$ listing_url                                  <chr> "https://www.airbnb.com/rooms/6983", "https://www.airbnb.com/ro…
$ scrape_id                                    <dbl> 20200626200423, 20200626200423, 20200626200423, 20200626200423,…
$ last_scraped                                 <date> 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-29, 20…
$ name                                         <chr> "Copenhagen 'N Livin'", "Lovely house - most attractive area", …
$ summary                                      <chr> "Lovely apartment located in the hip Nørrebro area, close to ba…
$ space                                        <chr> "Beautiful and cosy apartment conveniently located in the hip N…
$ description                                  <chr> "Lovely apartment located in the hip Nørrebro area, close to ba…
$ experiences_offered                          <chr> "none", "none", "none", "none", "none", "none", "none", "none",…
$ neighborhood_overview                        <chr> "Nice bars and cozy cafes just minutes away, yet the street its…
$ notes                                        <chr> NA, NA, NA, NA, "Please note that the bed in the second bedroom…
$ transit                                      <chr> "Bus 66 runs to the central station. Forum metro is about 10 mi…
$ access                                       <chr> "Bedroom, living room, kitchen, and bathroom for shared use. Yo…
$ interaction                                  <chr> "We are usually at work during day time, but will be home most …
$ house_rules                                  <chr> "No smoking allowed! No pets.", "We will leave the house clean …
$ thumbnail_url                                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ medium_url                                   <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ picture_url                                  <chr> "https://a0.muscache.com/im/pictures/42044170/f63c4d99_original…
$ xl_picture_url                               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ host_id                                      <dbl> 16774, 109777, 112210, 125230, 127577, 129976, 140105, 142143, …
$ host_url                                     <chr> "https://www.airbnb.com/users/show/16774", "https://www.airbnb.…
$ host_name                                    <chr> "Simon", "Kari", "Oliver", "Nana", "Simon And Anna", "Ebbe", "L…
$ host_since                                   <date> 2009-05-12, 2010-04-17, 2010-04-22, 2010-05-15, 2010-05-18, 20…
$ host_location                                <chr> "Copenhagen, Capital Region of Denmark, Denmark", "Copenhagen, …
$ host_about                                   <chr> "I'm currently working as an environmental consultant for a lar…
$ host_response_time                           <chr> "N/A", "N/A", "within a few hours", "N/A", "N/A", "N/A", "withi…
$ host_response_rate                           <chr> "N/A", "N/A", "100%", "N/A", "N/A", "N/A", "100%", "N/A", "N/A"…
$ host_acceptance_rate                         <chr> "33%", "19%", "100%", "17%", "N/A", "N/A", "97%", "0%", "N/A", …
$ host_is_superhost                            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ host_thumbnail_url                           <chr> "https://a0.muscache.com/im/users/16774/profile_pic/1401276934/…
$ host_picture_url                             <chr> "https://a0.muscache.com/im/users/16774/profile_pic/1401276934/…
$ host_neighbourhood                           <chr> "Nørrebro", "Indre By", "Indre By", "Vesterbro", "Østerbro", "V…
$ host_listings_count                          <dbl> 1, 1, 4, 1, 1, 1, 3, 1, 0, 2, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, …
$ host_total_listings_count                    <dbl> 1, 1, 4, 1, 1, 1, 3, 1, 0, 2, 1, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, …
$ host_verifications                           <chr> "['email', 'phone', 'reviews']", "['email', 'phone', 'reviews',…
$ host_has_profile_pic                         <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
$ host_identity_verified                       <lgl> FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRU…
$ street                                       <chr> "Copenhagen, Hovedstaden, Denmark", "Copenhagen, Hovedstaden, D…
$ neighbourhood                                <chr> "Nørrebro", "Indre By", "Indre By", "Vesterbro", "Østerbro", "V…
$ neighbourhood_cleansed                       <chr> "Nrrebro", "Indre By", "Indre By", "Vesterbro-Kongens Enghave",…
$ neighbourhood_group_cleansed                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ city                                         <chr> "Copenhagen", "Copenhagen", "Copenhagen", "Copenhagen", "Copenh…
$ state                                        <chr> "Hovedstaden", "Hovedstaden", "Hovedstaden", "Hovedstaden", "Ho…
$ zipcode                                      <chr> "2200", "2100", "1210", "1650", "2100", "1719", "1620", "2100",…
$ market                                       <chr> "Copenhagen", "Copenhagen", "Copenhagen", "Copenhagen", "Copenh…
$ smart_location                               <chr> "Copenhagen, Denmark", "Copenhagen, Denmark", "Copenhagen, Denm…
$ country_code                                 <chr> "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK", "DK…
$ country                                      <chr> "Denmark", "Denmark", "Denmark", "Denmark", "Denmark", "Denmark…
$ latitude                                     <dbl> 55.68798, 55.69163, 55.67590, 55.67069, 55.69375, 55.66744, 55.…
$ longitude                                    <dbl> 12.54571, 12.57459, 12.57698, 12.55430, 12.56945, 12.55516, 12.…
$ is_location_exact                            <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TR…
$ property_type                                <chr> "Apartment", "House", "House", "Apartment", "Apartment", "Apart…
$ room_type                                    <chr> "Private room", "Entire home/apt", "Entire home/apt", "Entire h…
$ accommodates                                 <dbl> 2, 6, 12, 2, 4, 3, 3, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 6,…
$ bathrooms                                    <dbl> 1.0, 1.5, 2.5, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0…
$ bedrooms                                     <dbl> 1, 4, 6, 1, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, …
$ beds                                         <dbl> 1, 4, 7, 1, 3, 3, 2, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 2, 4, …
$ bed_type                                     <chr> "Real Bed", "Real Bed", "Real Bed", "Real Bed", "Real Bed", "Re…
$ amenities                                    <chr> "{TV,\"Cable TV\",Wifi,Kitchen,\"Paid parking off premises\",He…
$ square_feet                                  <dbl> 97, NA, NA, NA, NA, 689, NA, 807, NA, 420, 161, NA, 527, NA, NA…
$ price                                        <chr> "$365.00", "$2,398.00", "$3,096.00", "$797.00", "$857.00", "$75…
$ weekly_price                                 <chr> NA, NA, "$17,513.00", NA, "$2,981.00", "$4,700.00", "$7,453.00"…
$ monthly_price                                <chr> NA, NA, "$67,073.00", NA, "$8,943.00", NA, "$26,084.00", "$14,1…
$ security_deposit                             <chr> "$0.00", "$5,000.00", "$3,726.00", NA, NA, "$1,000.00", "$0.00"…
$ cleaning_fee                                 <chr> "$33.00", "$1,100.00", "$522.00", "$300.00", "$75.00", NA, "$0.…
$ guests_included                              <dbl> 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 3, 2, 4, …
$ extra_people                                 <chr> "$66.00", "$350.00", "$0.00", "$0.00", "$0.00", "$100.00", "$33…
$ minimum_nights                               <dbl> 2, 3, 3, 7, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ maximum_nights                               <dbl> 15, 30, 31, 14, 31, 10, 365, 1125, 21, 90, 30, 730, 14, 15, 112…
$ minimum_minimum_nights                       <dbl> 2, 3, 3, 3, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ maximum_minimum_nights                       <dbl> 2, 3, 3, 5, 7, 2, 3, 6, 5, 30, 1, 3, 5, 3, 2, 4, 3, 4, 9, 4, 3,…
$ minimum_maximum_nights                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ maximum_maximum_nights                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ minimum_nights_avg_ntm                       <dbl> 2.0, 3.0, 3.0, 4.1, 7.0, 2.0, 3.0, 6.0, 5.0, 30.0, 1.0, 3.0, 5.…
$ maximum_nights_avg_ntm                       <dbl> 15, 30, 1125, 14, 1125, 10, 1125, 1125, 1125, 90, 30, 730, 14, …
$ calendar_updated                             <chr> "5 months ago", "4 months ago", "7 months ago", "4 months ago",…
$ has_availability                             <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
$ availability_30                              <dbl> 29, 28, 29, 21, 0, 0, 8, 0, 11, 0, 0, 0, 2, 29, 29, 0, 0, 6, 0,…
$ availability_60                              <dbl> 59, 58, 59, 21, 0, 0, 8, 0, 24, 0, 0, 0, 13, 59, 59, 0, 0, 36, …
$ availability_90                              <dbl> 89, 88, 89, 21, 0, 0, 8, 5, 24, 26, 0, 0, 43, 89, 89, 0, 0, 66,…
$ availability_365                             <dbl> 89, 363, 172, 21, 0, 58, 8, 189, 24, 301, 0, 0, 133, 364, 89, 0…
$ calendar_last_scraped                        <date> 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-28, 2020-06-29, 20…
$ number_of_reviews                            <dbl> 168, 50, 293, 22, 90, 17, 73, 7, 40, 16, 184, 9, 74, 67, 92, 7,…
$ number_of_reviews_ltm                        <dbl> 1, 4, 31, 2, 0, 0, 1, 0, 0, 1, 11, 1, 2, 1, 13, 0, 0, 11, 0, 0,…
$ first_review                                 <date> 2009-09-04, 2013-12-02, 2010-10-14, 2010-06-17, 2010-08-16, 20…
$ last_review                                  <date> 2019-07-19, 2019-12-14, 2020-03-02, 2019-08-02, 2017-06-03, 20…
$ review_scores_rating                         <dbl> 96, 98, 91, 98, 94, 97, 98, 91, 97, 89, 92, 98, 94, 95, 94, 100…
$ review_scores_accuracy                       <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10, 9, 9, 9, 10, 9, …
$ review_scores_cleanliness                    <dbl> 9, 10, 9, 10, 9, 10, 10, 9, 9, 8, 8, 9, 9, 9, 9, 10, 9, 10, 10,…
$ review_scores_checkin                        <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
$ review_scores_communication                  <dbl> 10, 10, 10, 10, 9, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 10, 1…
$ review_scores_location                       <dbl> 9, 10, 10, 10, 10, 10, 10, 9, 9, 10, 10, 9, 10, 10, 9, 10, 9, 1…
$ review_scores_value                          <dbl> 9, 10, 9, 10, 9, 9, 9, 9, 10, 9, 9, 9, 9, 9, 9, 10, 9, 10, 10, …
$ requires_license                             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ license                                      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ jurisdiction_names                           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ instant_bookable                             <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FA…
$ is_business_travel_ready                     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ cancellation_policy                          <chr> "moderate", "moderate", "moderate", "strict_14_with_grace_perio…
$ require_guest_profile_picture                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ require_guest_phone_verification             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ calculated_host_listings_count               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ calculated_host_listings_count_entire_homes  <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, …
$ calculated_host_listings_count_private_rooms <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
$ calculated_host_listings_count_shared_rooms  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ reviews_per_month                            <dbl> 1.28, 0.62, 2.48, 0.18, 0.75, 0.14, 0.61, 0.06, 0.33, 0.14, 1.5…

#calendar <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/calendar.csv.gz')
#calendar %>% glimpse()

reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/reviews.csv.gz')
reviews %>% glimpse()

Rows: 386,954
Columns: 6
$ listing_id    <dbl> 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, 6983, …
$ id            <dbl> 8985, 9199, 11763, 20616, 23168, 33706, 40860, 51622, 54476, 56504, 1073551, 1133218, 1159109, 1179503…
$ date          <date> 2009-09-04, 2009-09-07, 2009-09-28, 2009-12-17, 2010-01-04, 2010-04-05, 2010-05-08, 2010-06-10, 2010-…
$ reviewer_id   <dbl> 31133, 11472, 35317, 53151, 50856, 96881, 113545, 135677, 138676, 141069, 366694, 977908, 1960762, 206…
$ reviewer_name <chr> "Jeff", "Andrea", "Jeanine", "Sonja", "James", "Tom", "Orion", "Gonzalo", "Tiffany", "Payton", "Ruifen…
$ comments      <chr> "Convenient location, clean apartment, great breakfast...couldn't have asked for more. Best value in l…

# # And the summary plus geodata
# summaries_listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/listings.csv')
# summaries_reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/reviews.csv')
# summaries_neighbourhoods <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.csv')

# The geodat of the hoods comes as a geojson, so we need the right package to load it
library(geojsonio)
neighbourhoods_geojson <- geojson_read( 'http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.geojson',  what = "sp")

Problem 1: Professional host

listings %>%
  count(host_id, sort = TRUE)

listings %>%
  filter(host_id == 187610263) %>%
  count(neighbourhood_cleansed, sort = TRUE)

listings %<>%
  mutate(price = price %>% parse_number(),
         price_sqf = price / square_feet)

listings %<>%
  group_by(host_id) %>%
  mutate(host_professional = n() >= 5) %>%
  ungroup()

listings %>%
  group_by(host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE),
            price = price %>% mean(na.rm = TRUE))

listings %>%
  group_by(neighbourhood_cleansed, host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE)) %>%
  pivot_wider(names_from = host_professional, values_from = review)

Description & Satisfaction

listings %<>%
  mutate(desc_lenght = description %>% str_count('\\w+')) %>%
  mutate(desc_long =  percent_rank(desc_lenght) > 0.9 )

listings %>%
  group_by(desc_long) %>%
  summarise(review = review_scores_rating %>% mean(na.rm =TRUE))

Inspecting & Tidying data

Basic formating

listings %>% skim()

── Data Summary ────────────────────────
                           Values    
Name                       Piped data
Number of rows             28523     
Number of columns          110       
_______________________              
Column type frequency:               
  character                45        
  Date                     5         
  logical                  18        
  numeric                  42        
________________________             
Group variables            None      

── Variable type: character ───────────────────────────────────────────────────────────────────────────────────────────
   skim_variable          n_missing complete_rate   min   max empty n_unique whitespace
 1 listing_url                    0        1         33    37     0    28523          0
 2 name                          57        0.998      1   211     0    26906          0
 3 summary                     1096        0.962      1  1000     0    26981          0
 4 space                      11390        0.601      1  1000     0    16804          0
 5 description                  515        0.982      1  1000     0    27748          0
 6 experiences_offered            0        1          4     4     0        1          0
 7 neighborhood_overview      12407        0.565      1  1000     0    15514          0
 8 notes                      20832        0.270      1  1000     0     7365          0
 9 transit                    11316        0.603      1  1000     0    16711          0
10 access                     15751        0.448      1  1000     0    11249          0
11 interaction                14111        0.505      1  1000     0    13726          0
12 house_rules                13332        0.533      1  1000     0    13703          0
13 picture_url                    0        1         81   146     0    28282          0
14 host_url                       0        1         37    43     0    25745          0
15 host_name                     12        1.00       1    34     0     6415          0
16 host_location                 93        0.997      2   151     0      863          0
17 host_about                 14028        0.508      1  3550     0    12476         34
18 host_response_time            11        1.00       3    18     0        5          0
19 host_response_rate            11        1.00       2     4     0       44          0
20 host_acceptance_rate          11        1.00       2     4     0      100          0
21 host_thumbnail_url            11        1.00      55   106     0    25669          0
22 host_picture_url              11        1.00      57   109     0    25669          0
23 host_neighbourhood          8007        0.719      1    21     0       56          0
24 host_verifications             0        1          2   156     0      316          0
25 street                         0        1         10    61     0      527          0
26 neighbourhood                  0        1          5    14     0       21          0
27 neighbourhood_cleansed         0        1          5    25     0       11          0
28 city                          13        1.00       1    26     0      134          0
29 state                      24042        0.157      1    25     0      170          0
30 zipcode                      810        0.972      3    17     0      454          0
31 market                       864        0.970      6    21     0       10          0
32 smart_location                 0        1         10    35     0      157          0
33 country_code                   0        1          2     2     0        1          0
34 country                        0        1          7     7     0        1          0
35 property_type                  0        1          3    22     0       29          0
36 room_type                      0        1         10    15     0        4          0
37 bed_type                       0        1          5    13     0        5          0
38 amenities                      0        1          2  1179     0    26634          0
39 weekly_price               25008        0.123      7    11     0      718          0
40 monthly_price              26971        0.0544     7    11     0      476          0
41 security_deposit           13845        0.515      5    10     0      386          0
42 cleaning_fee                8968        0.686      5     9     0      430          0
43 extra_people                   0        1          5     9     0      265          0
44 calendar_updated               0        1          5    13     0       82          0
45 cancellation_policy            0        1          8    27     0        4          0

── Variable type: Date ────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable         n_missing complete_rate min        max        median     n_unique
1 last_scraped                  0         1     2020-06-26 2020-06-30 2020-06-27        5
2 host_since                   11         1.00  2008-06-27 2020-06-26 2015-06-22     3379
3 calendar_last_scraped         0         1     2020-06-26 2020-06-30 2020-06-27        5
4 first_review               4968         0.826 2009-09-04 2020-06-28 2017-08-07     2582
5 last_review                4968         0.826 2011-08-10 2020-06-28 2019-07-29     1846

── Variable type: logical ─────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable                    n_missing complete_rate      mean count                   
 1 thumbnail_url                        28523         0     NaN       ": "                    
 2 medium_url                           28523         0     NaN       ": "                    
 3 xl_picture_url                       28523         0     NaN       ": "                    
 4 host_is_superhost                       11         1.00    0.102   "FAL: 25610, TRU: 2902" 
 5 host_has_profile_pic                    11         1.00    0.998   "TRU: 28445, FAL: 67"   
 6 host_identity_verified                  11         1.00    0.376   "FAL: 17790, TRU: 10722"
 7 neighbourhood_group_cleansed         28523         0     NaN       ": "                    
 8 is_location_exact                        0         1       0.789   "TRU: 22507, FAL: 6016" 
 9 has_availability                         0         1       1       "TRU: 28523"            
10 requires_license                         0         1       0       "FAL: 28523"            
11 license                              28523         0     NaN       ": "                    
12 jurisdiction_names                   28523         0     NaN       ": "                    
13 instant_bookable                         0         1       0.286   "FAL: 20360, TRU: 8163" 
14 is_business_travel_ready                 0         1       0       "FAL: 28523"            
15 require_guest_profile_picture            0         1       0.00505 "FAL: 28379, TRU: 144"  
16 require_guest_phone_verification         0         1       0.00687 "FAL: 28327, TRU: 196"  
17 host_professional                        0         1       0.0305  "FAL: 27653, TRU: 870"  
18 desc_long                              515         0.982   0.0972  "FAL: 25285, TRU: 2723" 

── Variable type: numeric ─────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable                                n_missing complete_rate       mean            sd       p0      p25
 1 id                                                   0        1        2.04e+ 7 12261143.     6.98e+ 3 1.01e+ 7
 2 scrape_id                                            0        1        2.02e+13        0      2.02e+13 2.02e+13
 3 host_id                                              0        1        6.73e+ 7 76172316.     5.13e+ 2 1.28e+ 7
 4 host_listings_count                                 11        1.00     5.46e+ 0       34.3    0.       1.00e+ 0
 5 host_total_listings_count                           11        1.00     5.46e+ 0       34.3    0.       1.00e+ 0
 6 latitude                                             0        1        5.57e+ 1        0.0191 5.56e+ 1 5.57e+ 1
 7 longitude                                            0        1        1.26e+ 1        0.0317 1.24e+ 1 1.25e+ 1
 8 accommodates                                         0        1        3.32e+ 0        1.63   1.00e+ 0 2.00e+ 0
 9 bathrooms                                           12        1.00     1.08e+ 0        0.284  0.       1.00e+ 0
10 bedrooms                                            29        0.999    1.55e+ 0        1.06   0.       1.00e+ 0
11 beds                                               103        0.996    2.04e+ 0        1.44   0.       1.00e+ 0
12 square_feet                                      28130        0.0138   7.22e+ 2      576.     0.       1.20e+ 2
13 price                                                0        1        8.49e+ 2     1067.     0.       4.98e+ 2
14 guests_included                                      0        1        1.52e+ 0        1.06   1.00e+ 0 1.00e+ 0
15 minimum_nights                                       0        1        3.85e+ 0       18.1    1.00e+ 0 2.00e+ 0
16 maximum_nights                                       0        1        6.21e+ 2      553.     1.00e+ 0 1.50e+ 1
17 minimum_minimum_nights                               0        1        3.84e+ 0       18.1    1.00e+ 0 2.00e+ 0
18 maximum_minimum_nights                               0        1        4.12e+ 0       19.3    1.00e+ 0 2.00e+ 0
19 minimum_maximum_nights                               0        1        6.67e+ 2      548.     1.00e+ 0 2.00e+ 1
20 maximum_maximum_nights                               0        1        6.70e+ 2      547.     1.00e+ 0 2.00e+ 1
21 minimum_nights_avg_ntm                               0        1        3.97e+ 0       18.3    1.00e+ 0 2.00e+ 0
22 maximum_nights_avg_ntm                               0        1        6.68e+ 2      547.     1.00e+ 0 2.00e+ 1
23 availability_30                                      0        1        5.87e+ 0       10.4    0.       0.      
24 availability_60                                      0        1        1.12e+ 1       20.1    0.       0.      
25 availability_90                                      0        1        1.65e+ 1       30.0    0.       0.      
26 availability_365                                     0        1        4.95e+ 1       99.2    0.       0.      
27 number_of_reviews                                    0        1        1.36e+ 1       27.1    0.       1.00e+ 0
28 number_of_reviews_ltm                                0        1        2.74e+ 0        6.64   0.       0.      
29 review_scores_rating                              5447        0.809    9.52e+ 1        6.83   2.00e+ 1 9.30e+ 1
30 review_scores_accuracy                            5468        0.808    9.73e+ 0        0.655  2.00e+ 0 1.00e+ 1
31 review_scores_cleanliness                         5466        0.808    9.38e+ 0        0.956  2.00e+ 0 9.00e+ 0
32 review_scores_checkin                             5488        0.808    9.82e+ 0        0.561  2.00e+ 0 1.00e+ 1
33 review_scores_communication                       5470        0.808    9.86e+ 0        0.529  2.00e+ 0 1.00e+ 1
34 review_scores_location                            5491        0.807    9.60e+ 0        0.678  2.00e+ 0 9.00e+ 0
35 review_scores_value                               5495        0.807    9.45e+ 0        0.763  2.00e+ 0 9.00e+ 0
36 calculated_host_listings_count                       0        1        4.45e+ 0       28.1    1.00e+ 0 1.00e+ 0
37 calculated_host_listings_count_entire_homes          0        1        4.17e+ 0       28.1    0.       1.00e+ 0
38 calculated_host_listings_count_private_rooms         0        1        2.58e- 1        0.731  0.       0.      
39 calculated_host_listings_count_shared_rooms          0        1        1.22e- 2        0.325  0.       0.      
40 reviews_per_month                                 4968        0.826    4.95e- 1        0.727  1.00e- 2 1.20e- 1
41 price_sqf                                        28130        0.0138 Inf             NaN      3.36e- 1 8.22e- 1
42 desc_lenght                                        515        0.982    1.26e+ 2       56.3    0.       7.70e+ 1
        p50      p75      p100 hist 
 1 1.95e+ 7 3.04e+ 7   4.40e 7 ▇▇▇▅▆
 2 2.02e+13 2.02e+13   2.02e13 ▁▁▇▁▁
 3 3.60e+ 7 9.46e+ 7   3.52e 8 ▇▂▁▁▁
 4 1.00e+ 0 1.00e+ 0   7.37e 2 ▇▁▁▁▁
 5 1.00e+ 0 1.00e+ 0   7.37e 2 ▇▁▁▁▁
 6 5.57e+ 1 5.57e+ 1   5.57e 1 ▁▃▇▇▁
 7 1.26e+ 1 1.26e+ 1   1.26e 1 ▁▂▇▅▂
 8 3.00e+ 0 4.00e+ 0   1.60e 1 ▇▂▁▁▁
 9 1.00e+ 0 1.00e+ 0   1.00e 1 ▇▁▁▁▁
10 1.00e+ 0 2.00e+ 0   1.01e 2 ▇▁▁▁▁
11 2.00e+ 0 3.00e+ 0   2.50e 1 ▇▁▁▁▁
12 7.64e+ 2 1.08e+ 3   2.80e 3 ▇▇▃▁▁
13 6.98e+ 2 9.96e+ 2   6.92e 4 ▇▁▁▁▁
14 1.00e+ 0 2.00e+ 0   1.60e 1 ▇▁▁▁▁
15 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
16 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
17 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
18 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
19 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
20 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
21 3.00e+ 0 4.00e+ 0   1.10e 3 ▇▁▁▁▁
22 1.12e+ 3 1.12e+ 3   1.00e 4 ▇▁▁▁▁
23 0.       8.00e+ 0   3.00e 1 ▇▁▁▁▂
24 0.       1.40e+ 1   6.00e 1 ▇▁▁▁▂
25 0.       1.80e+ 1   9.00e 1 ▇▁▁▁▁
26 0.       3.50e+ 1   3.65e 2 ▇▁▁▁▁
27 5.00e+ 0 1.50e+ 1   6.37e 2 ▇▁▁▁▁
28 0.       3.00e+ 0   3.76e 2 ▇▁▁▁▁
29 9.70e+ 1 1.00e+ 2   1.00e 2 ▁▁▁▁▇
30 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
31 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
32 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
33 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
34 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
35 1.00e+ 1 1.00e+ 1   1.00e 1 ▁▁▁▁▇
36 1.00e+ 0 1.00e+ 0   2.81e 2 ▇▁▁▁▁
37 1.00e+ 0 1.00e+ 0   2.81e 2 ▇▁▁▁▁
38 0.       0.         1.20e 1 ▇▁▁▁▁
39 0.       0.         1.30e 1 ▇▁▁▁▁
40 2.80e- 1 5.90e- 1   3.06e 1 ▇▁▁▁▁
41 1.12e+ 0 6.59e+ 0 Inf       ▇▁▁▁▁
42 1.51e+ 2 1.75e+ 2   2.10e 2 ▂▃▃▅▇

listings %<>%
    mutate(across(is_character, ~ifelse(.x == "", NA, .x)))

Misssing data

library(VIM)

listings %>% 
  select(host_is_superhost, review_scores_rating, host_response_time, name, host_since,zipcode) %>%
  aggr(numbers = TRUE, prop = c(TRUE, FALSE))

Best party place

listings %<>% 
  mutate(party_place = accommodates >= 10)

listings %>% 
  filter(party_place == TRUE) %>%
  group_by(neighbourhood_cleansed) %>%
  summarize(n = n(),
         review = review_scores_rating %>% mean(na.rm = TRUE),
         price = price %>% mean(na.rm = TRUE) ) %>%
  arrange(desc(n))

EDA

DataViz

Geoplotting

mad_map <- get_map(getbb("Madrid"), maptype = "toner-background")

Error in getbb("Madrid") : could not find function "getbb"

listings %>% leaflet() %>%
  addTiles() %>%
  addMarkers(~longitude, ~latitude,
             labelOptions = labelOptions(noHide = F),
             clusterOptions = markerClusterOptions(),
             popup = paste0("<b> Name: </b>", listings$name, 
                            "<br/><b> Host Name: </b>", listings$host_name, 
                            "<br> <b> Price: </b>", listings$price, 
                            "<br/><b> Room Type: </b>", listings$room_type, 
                            "<br/><b> Property Type: </b>", listings$property_type
                 )) %>% 
#  setView(-74.00, 40.71, zoom = 12) %>%
  addProviderTiles("CartoDB.Positron")

# I need to fortify the data AND keep trace of the commune code! (Takes ~2 minutes)
library(broom)
neighbourhoods_tidy <-  neighbourhoods_geojson %>%
  tidy(region = "neighbourhood")

neighbourhoods_tidy %>% glimpse()

Rows: 6,658
Columns: 7
$ long  <dbl> 12.63094, 12.63126, 12.63221, 12.63160, 12.63154, 12.63153, 12.63153, 12.63153, 12.63157, 12.63158, 12…
$ lat   <dbl> 55.67050, 55.67028, 55.66961, 55.66943, 55.66941, 55.66940, 55.66939, 55.66930, 55.66926, 55.66924, 55…
$ order <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,…
$ hole  <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
$ piece <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ group <fct> Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1, Amager st.1…
$ id    <chr> "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st", "Amager st"…

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  theme_void() +
  coord_map()

neighborhood_agg <- listings %>%
  group_by(neighbourhood_cleansed) %>%
  summarise(n = n(),
            price_mean = price %>% mean(na.rm = TRUE),
            review_mean = review_scores_rating %>% mean(na.rm = TRUE))

neighbourhoods_tidy %<>%
  left_join(neighborhood_agg, by = c('id' = 'neighbourhood_cleansed'))

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = n)) +
  geom_polygon() +
  theme_void() +
  coord_map()

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = price_mean)) +
  geom_polygon() +
  theme_void() +
  coord_map()

neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = review_mean)) +
  geom_polygon() +
  theme_void() +
  coord_map()

Subervised ML

Inspect again…

listings %>% skim(neighbourhood_cleansed, accommodates, price, room_type, host_is_superhost )

── Data Summary ────────────────────────
                           Values    
Name                       Piped data
Number of rows             28523     
Number of columns          111       
_______________________              
Column type frequency:               
  character                2         
  logical                  1         
  numeric                  2         
________________________             
Group variables            None      

── Variable type: character ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable          n_missing complete_rate   min   max empty n_unique whitespace
1 neighbourhood_cleansed         0             1     5    25     0       11          0
2 room_type                      0             1    10    15     0        4          0

── Variable type: logical ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable     n_missing complete_rate  mean count                
1 host_is_superhost        11          1.00 0.102 FAL: 25610, TRU: 2902

── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable n_missing complete_rate   mean      sd    p0   p25   p50   p75  p100 hist 
1 accommodates          0             1   3.32    1.63     1     2     3     4    16 ▇▂▁▁▁
2 price                 0             1 849.   1067.       0   498   698   996 69175 ▇▁▁▁▁

data <- listings %>% 
  select(review_scores_rating, neighbourhood_cleansed, accommodates, price, room_type, host_is_superhost) %>%
  rename(y = price) %>%
  relocate(y, everything()) %>%
  drop_na()

data %>% count(neighbourhood_cleansed, sort = TRUE)

data %>% count(room_type, sort = TRUE)

data %<>%
  filter(!(room_type %in% c('hared room', 'Hotel room')))

data %<>%
  filter(percent_rank(y) <0.99)

data %>%
  skim()

── Data Summary ────────────────────────
                           Values    
Name                       Piped data
Number of rows             22807     
Number of columns          6         
_______________________              
Column type frequency:               
  character                2         
  logical                  1         
  numeric                  3         
________________________             
Group variables            None      

── Variable type: character ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable          n_missing complete_rate   min   max empty n_unique whitespace
1 neighbourhood_cleansed         0             1     5    25     0       11          0
2 room_type                      0             1    11    15     0        3          0

── Variable type: logical ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable     n_missing complete_rate  mean count                
1 host_is_superhost         0             1 0.121 FAL: 20048, TRU: 2759

── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable        n_missing complete_rate   mean     sd    p0   p25   p50   p75  p100 hist 
1 y                            0             1 771.   391.       0   498   698   950  2890 ▅▇▂▁▁
2 review_scores_rating         0             1  95.2    6.79    20    93    97   100   100 ▁▁▁▁▇
3 accommodates                 0             1   3.25   1.55     1     2     3     4    16 ▇▂▁▁▁

---
title: "Workshop: Exploring the InsideAirBnB dataset"
author: "Daniel S. Hain (dsh@business.aau.dk)"
date: "Updated `r format(Sys.time(), '%B %d, %Y')`"
output:
  html_notebook:
    code_folding: show
    df_print: paged
    toc: true
    toc_depth: 2
    toc_float:
      collapsed: false
    theme: flatly
---

```{r setup, include=FALSE}
# Knitr options
### Generic preamble
Sys.setenv(LANG = "en") # For english language
options(scipen = 5) # To deactivate annoying scientific number notation

# rm(list=ls()); graphics.off() # get rid of everything in the workspace
if (!require("knitr")) install.packages("knitr"); library(knitr) # For display of the markdown

### Knitr options
knitr::opts_chunk$set(warning=FALSE,
                     message=FALSE,
                     fig.align="center"
                     )
```

## Preamble

```{r}
# Clear workspace
rm(list=ls()); graphics.off() 
```

```{r}
### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.
library(magrittr) # For extra-piping operators (eg. %<>%)
library(skimr) # For nice data summaries
```


# The InsideAirBnB data

## Instroduction


* The data is sourced from the [**Inside Airbnb**](http://insideairbnb.com/get-the-data.html) which hosts publicly available data from the Airbnb site.
* Interactive visualizations are provided [here](http://insideairbnb.com/copenhagen/?neighbourhood=&filterEntireHomes=false&filterHighlyAvailable=false&filterRecentReviews=false&filterMultiListings=false)

The dataset comprises of three main tables:

* `listings` - Detailed listings data showing 96 atttributes for each of the listings. Some of the attributes which are intuitivly interesting are: `price` (continuous), `longitude` (continuous), `latitude` (continuous), `listing_type` (categorical), `is_superhost` (categorical), `neighbourhood` (categorical), `ratings` (continuous) among others.
* `reviews` - Detailed reviews given by the guests with 6 attributes. Key attributes include `date` (datetime), `listing_id` (discrete), `reviewer_id` (discrete) and `comment` (textual).
* `calendar` - Provides details about booking for the next year by listing. Four attributes in total including `listing_id` (discrete), `date` (datetime), `available` (categorical) and `price` (continuous).

## Load data

```{r}
listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/listings.csv.gz')
listings %>% glimpse()
```

```{r}
#calendar <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/calendar.csv.gz')
#calendar %>% glimpse()
```

```{r}
reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/data/reviews.csv.gz')
reviews %>% glimpse()
```

```{r}
# # And the summary plus geodata
# summaries_listings <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/listings.csv')
# summaries_reviews <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/reviews.csv')
# summaries_neighbourhoods <- read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.csv')
```
```{r}
# The geodat of the hoods comes as a geojson, so we need the right package to load it
library(geojsonio)
neighbourhoods_geojson <- geojson_read( 'http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-06-26/visualisations/neighbourhoods.geojson',  what = "sp")
```

# Problem 1: Professional host

```{r}
listings %>%
  count(host_id, sort = TRUE)
```

```{r}
listings %>%
  filter(host_id == 187610263) %>%
  count(neighbourhood_cleansed, sort = TRUE)
```

```{r}
listings %<>%
  mutate(price = price %>% parse_number(),
         price_sqf = price / square_feet) 
```

```{r}
listings %<>%
  group_by(host_id) %>%
  mutate(host_professional = n() >= 5) %>%
  ungroup()
```

```{r}
listings %>%
  group_by(host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE),
            price = price %>% mean(na.rm = TRUE))
```

```{r}
listings %>%
  group_by(neighbourhood_cleansed, host_professional) %>%
  summarise(review = review_scores_rating %>% mean(na.rm = TRUE)) %>%
  pivot_wider(names_from = host_professional, values_from = review)
```


# Description & Satisfaction

```{r}
listings %<>%
  mutate(desc_lenght = description %>% str_count('\\w+')) %>%
  mutate(desc_long =  percent_rank(desc_lenght) > 0.9 )
```

```{r}
listings %>%
  group_by(desc_long) %>%
  summarise(review = review_scores_rating %>% mean(na.rm =TRUE))
```









# Inspecting & Tidying data

## Basic formating

```{r}
listings %>% skim()
```

```{r}
listings %<>%
    mutate(across(is_character, ~ifelse(.x == "", NA, .x)))
```


## Misssing data

```{r}
library(VIM)
```

```{r}
listings %>% 
  select(host_is_superhost, review_scores_rating, host_response_time, name, host_since,zipcode) %>%
  aggr(numbers = TRUE, prop = c(TRUE, FALSE))
```

# Best party place

```{r}
listings %<>% 
  mutate(party_place = accommodates >= 10) 
```

```{r}
listings %>% 
  filter(party_place == TRUE) %>%
  group_by(neighbourhood_cleansed) %>%
  summarize(n = n(),
         review = review_scores_rating %>% mean(na.rm = TRUE),
         price = price %>% mean(na.rm = TRUE) ) %>%
  arrange(desc(n))
```


# EDA























# DataViz

# Geoplotting

```{r}
library(leaflet)
```

```{r}
listings %>% leaflet() %>%
  addTiles() %>%
  addMarkers(~longitude, ~latitude,
             labelOptions = labelOptions(noHide = F),
             clusterOptions = markerClusterOptions(),
             popup = paste0("<b> Name: </b>", listings$name, 
                            "<br/><b> Host Name: </b>", listings$host_name, 
                            "<br> <b> Price: </b>", listings$price, 
                            "<br/><b> Room Type: </b>", listings$room_type, 
                            "<br/><b> Property Type: </b>", listings$property_type
                 )) %>% 
#  setView(-74.00, 40.71, zoom = 12) %>%
  addProviderTiles("CartoDB.Positron")
```

```{r}
# I need to fortify the data AND keep trace of the commune code! (Takes ~2 minutes)
library(broom)
neighbourhoods_tidy <-  neighbourhoods_geojson %>%
  tidy(region = "neighbourhood")
```

```{r}
neighbourhoods_tidy %>% glimpse()
```

```{r}
neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  theme_void() +
  coord_map()
```
```{r}
neighborhood_agg <- listings %>%
  group_by(neighbourhood_cleansed) %>%
  summarise(n = n(),
            price_mean = price %>% mean(na.rm = TRUE),
            review_mean = review_scores_rating %>% mean(na.rm = TRUE))
  
```

```{r}
neighbourhoods_tidy %<>%
  left_join(neighborhood_agg, by = c('id' = 'neighbourhood_cleansed'))
```

```{r}
neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = n)) +
  geom_polygon() +
  theme_void() +
  coord_map()
```

```{r}
neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = price_mean)) +
  geom_polygon() +
  theme_void() +
  coord_map()
```

```{r}
neighbourhoods_tidy %>%
  ggplot(aes(x = long, y = lat, group = group, fill = review_mean)) +
  geom_polygon() +
  theme_void() +
  coord_map()
```

# Subervised ML

Inspect again...

```{r}
listings %>% skim(neighbourhood_cleansed, accommodates, price, room_type, host_is_superhost)
```


```{r}
data <- listings %>% 
  select(review_scores_rating, neighbourhood_cleansed, accommodates, price, room_type, host_is_superhost) %>%
  rename(y = price) %>%
  relocate(y, everything()) %>%
  drop_na()
```

```{r}
data %>% count(neighbourhood_cleansed, sort = TRUE)
```

```{r}
data %>% count(room_type, sort = TRUE)
```

```{r}
data %<>%
  filter(!(room_type %in% c('hared room', 'Hotel room')))
```

```{r}
data %<>%
  filter(percent_rank(y) <0.99)
```

```{r}
data %>%
  skim()
```






Workshop: Exploring the InsideAirBnB dataset

Daniel S. Hain (dsh@business.aau.dk)

Updated September 23, 2020